






'''simple docstring'''







import inspect
import unittest

from transformers import ViTHybridConfig
from transformers.testing_utils import require_accelerate, require_torch, require_vision, slow, torch_device
from transformers.utils import cached_property, is_torch_available, is_vision_available

from ...test_configuration_common import ConfigTester
from ...test_modeling_common import ModelTesterMixin, _config_zero_init, floats_tensor, ids_tensor
from ...test_pipeline_mixin import PipelineTesterMixin


if is_torch_available():
  import torch
  from torch import nn

  from transformers import ViTHybridForImageClassification, ViTHybridImageProcessor, ViTHybridModel
  from transformers.models.vit_hybrid.modeling_vit_hybrid import VIT_HYBRID_PRETRAINED_MODEL_ARCHIVE_LIST


if is_vision_available():
  from PIL import Image




class   __SCREAMING_SNAKE_CASE       :


  """simple docstring"""


  def __init__(			self   ,	UpperCamelCase__   ,	UpperCamelCase__=13   ,	UpperCamelCase__=64   ,	UpperCamelCase__=2   ,	UpperCamelCase__=3   ,	UpperCamelCase__=True   ,	UpperCamelCase__=True   ,	UpperCamelCase__=32   ,	UpperCamelCase__=5   ,	UpperCamelCase__=4   ,	UpperCamelCase__=37   ,	UpperCamelCase__="gelu"   ,	UpperCamelCase__=0.1   ,	UpperCamelCase__=0.1   ,	UpperCamelCase__=10   ,	UpperCamelCase__=0.02   ,	UpperCamelCase__=[1, 16, 4, 4]   ,	UpperCamelCase__=None   ,	):






         """simple docstring"""


         a_						     =			parent
         a_						     =			batch_size
         a_						     =			image_size
         a_						     =			patch_size
         a_						     =			num_channels
         a_						     =			is_training
         a_						     =			use_labels
         a_						     =			hidden_size
         a_						     =			num_hidden_layers
         a_						     =			num_attention_heads
         a_						     =			intermediate_size
         a_						     =			hidden_act
         a_						     =			hidden_dropout_prob
         a_						     =			attention_probs_dropout_prob
         a_						     =			type_sequence_label_size
         a_						     =			initializer_range
         a_						     =			scope
         a_						     =			backbone_featmap_shape

         # in ViT hybrid, the seq length equals the number of patches + 1 (we add 1 for the [CLS] token)
         # the number of patches is based on the feature map of the backbone, which by default uses an output stride
         # of 32, which means that the feature map has a spatial resolution of 1/32 of the input image size
         a_						     =			(self.image_size // 32) ** 2
         a_						     =			num_patches + 1







  def    _a       (			self	):






         """simple docstring"""


         a_						     =			floats_tensor([self.batch_size, self.num_channels, self.image_size, self.image_size]	)

         a_						     =			None
         if self.use_labels:
                a_						     =			ids_tensor([self.batch_size]   ,	self.type_sequence_label_size	)

         a_						     =			self.get_config()

         return config, pixel_values, labels







  def    _a       (			self	):






         """simple docstring"""


         a_						     =			{
             'global_padding': 'same',
             'layer_type': 'bottleneck',
             'depths': [3, 4, 9],
             'out_features': ['stage1', 'stage2', 'stage3'],
             'embedding_dynamic_padding': True,
             'hidden_sizes': [4, 8, 16, 32],
             'num_groups': 2,
         }

         return ViTHybridConfig(
             image_size=self.image_size   ,	patch_size=self.patch_size   ,	num_channels=self.num_channels   ,	hidden_size=self.hidden_size   ,	num_hidden_layers=self.num_hidden_layers   ,	num_attention_heads=self.num_attention_heads   ,	intermediate_size=self.intermediate_size   ,	hidden_act=self.hidden_act   ,	hidden_dropout_prob=self.hidden_dropout_prob   ,	attention_probs_dropout_prob=self.attention_probs_dropout_prob   ,	is_decoder=UpperCamelCase__   ,	initializer_range=self.initializer_range   ,	backbone_featmap_shape=self.backbone_featmap_shape   ,	backbone_config=UpperCamelCase__   ,	)







  def    _a       (			self   ,	UpperCamelCase__   ,	UpperCamelCase__   ,	UpperCamelCase__	):






         """simple docstring"""


         a_						     =			ViTHybridModel(config=UpperCamelCase__	)
         model.to(UpperCamelCase__	)
         model.eval()
         a_						     =			model(UpperCamelCase__	)
         self.parent.assertEqual(result.last_hidden_state.shape   ,	(self.batch_size, self.seq_length, self.hidden_size)	)







  def    _a       (			self   ,	UpperCamelCase__   ,	UpperCamelCase__   ,	UpperCamelCase__	):






         """simple docstring"""


         a_						     =			self.type_sequence_label_size
         a_						     =			ViTHybridForImageClassification(UpperCamelCase__	)
         model.to(UpperCamelCase__	)
         model.eval()
         a_						     =			model(UpperCamelCase__   ,	labels=UpperCamelCase__	)
         self.parent.assertEqual(result.logits.shape   ,	(self.batch_size, self.type_sequence_label_size)	)







  def    _a       (			self	):






         """simple docstring"""


         a_						     =			self.prepare_config_and_inputs()
         a_     ,	a_     ,	a_						     =			config_and_inputs
         a_						     =			{'pixel_values': pixel_values}
         return config, inputs_dict





@require_torch
class   __SCREAMING_SNAKE_CASE       (__A							,       __A							,       unittest.TestCase       ):


  """simple docstring"""


  _a  : Tuple      			=     (ViTHybridModel, ViTHybridForImageClassification) if is_torch_available() else ()
  _a  : str      			=     (
      {'''feature-extraction''': ViTHybridModel, '''image-classification''': ViTHybridForImageClassification}
      if is_torch_available()
      else {}
  )
  _a  : int      			=     False
  _a  : Union[str, Any]      			=     False
  _a  : str      			=     False







  def    _a       (			self	):






         """simple docstring"""


         a_						     =			ViTHybridModelTester(self	)
         a_						     =			ConfigTester(self   ,	config_class=UpperCamelCase__   ,	has_text_modality=UpperCamelCase__   ,	hidden_size=37	)







  def    _a       (			self	):






         """simple docstring"""


         self.config_tester.run_common_tests()







  @unittest.skip(reason='ViT does not use inputs_embeds'	)
  def    _a       (			self	):






         """simple docstring"""


         pass







  def    _a       (			self	):






         """simple docstring"""


         a_     ,	a_						     =			self.model_tester.prepare_config_and_inputs_for_common()

         for model_class in self.all_model_classes:
                a_						     =			model_class(UpperCamelCase__	)
                self.assertIsInstance(model.get_input_embeddings()   ,	(nn.Module)	)
                a_						     =			model.get_output_embeddings()
                self.assertTrue(x is None or isinstance(UpperCamelCase__   ,	nn.Linear	)	)







  def    _a       (			self	):






         """simple docstring"""


         a_     ,	a_						     =			self.model_tester.prepare_config_and_inputs_for_common()

         for model_class in self.all_model_classes:
                a_						     =			model_class(UpperCamelCase__	)
                a_						     =			inspect.signature(model.forward	)
                # signature.parameters is an OrderedDict => so arg_names order is deterministic
                a_						     =			[*signature.parameters.keys()]

                a_						     =			['pixel_values']
                self.assertListEqual(arg_names[:1]   ,	UpperCamelCase__	)







  def    _a       (			self	):






         """simple docstring"""


         a_						     =			self.model_tester.prepare_config_and_inputs()
         self.model_tester.create_and_check_model(*UpperCamelCase__	)







  def    _a       (			self	):






         """simple docstring"""


         a_						     =			self.model_tester.prepare_config_and_inputs()
         self.model_tester.create_and_check_for_image_classification(*UpperCamelCase__	)







  def    _a       (			self	):






         """simple docstring"""


         a_     ,	a_						     =			self.model_tester.prepare_config_and_inputs_for_common()

         a_						     =			_config_zero_init(UpperCamelCase__	)
         for model_class in self.all_model_classes:
                a_						     =			model_class(config=UpperCamelCase__	)
                # Skip the check for the backbone
                for name, module in model.named_modules():
                       if module.__class__.__name__ == "ViTHybridPatchEmbeddings":
                              a_						     =			[f'{name}.{key}' for key in module.state_dict().keys()]
                              break

                for name, param in model.named_parameters():
                       if param.requires_grad:
                              if name in backbone_params:
                                     continue
                              self.assertIn(
                                  ((param.data.mean() * 1e9).round() / 1e9).item()   ,	[0.0, 1.0]   ,	msg=f'Parameter {name} of model {model_class} seems not properly initialized'   ,	)







  @slow
  def    _a       (			self	):






         """simple docstring"""


         for model_name in VIT_HYBRID_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
                a_						     =			ViTHybridModel.from_pretrained(UpperCamelCase__	)
                self.assertIsNotNone(UpperCamelCase__	)





def       __UpperCamelCase					(      ):
       """simple docstring"""




       a_						     =			Image.open('./tests/fixtures/tests_samples/COCO/000000039769.png'   )
       return image




@require_torch
@require_vision
class   __SCREAMING_SNAKE_CASE       (unittest.TestCase       ):


  """simple docstring"""


  @cached_property
  def    _a       (			self	):






         """simple docstring"""


         return (
             ViTHybridImageProcessor.from_pretrained(VIT_HYBRID_PRETRAINED_MODEL_ARCHIVE_LIST[0]	)
             if is_vision_available()
             else None
         )







  @slow
  def    _a       (			self	):






         """simple docstring"""


         a_						     =			ViTHybridForImageClassification.from_pretrained(VIT_HYBRID_PRETRAINED_MODEL_ARCHIVE_LIST[0]	).to(
             UpperCamelCase__	)

         a_						     =			self.default_image_processor
         a_						     =			prepare_img()
         a_						     =			image_processor(images=UpperCamelCase__   ,	return_tensors='pt'	).to(UpperCamelCase__	)

         # forward pass
         with torch.no_grad():
                a_						     =			model(**UpperCamelCase__	)

         # verify the logits
         a_						     =			torch.Size((1, 1_000)	)
         self.assertEqual(outputs.logits.shape   ,	UpperCamelCase__	)

         a_						     =			torch.tensor([-1.9_090, -0.4_993, -0.2_389]	).to(UpperCamelCase__	)

         self.assertTrue(torch.allclose(outputs.logits[0, :3]   ,	UpperCamelCase__   ,	atol=1e-4	)	)







  @slow
  @require_accelerate
  def    _a       (			self	):






         """simple docstring"""


         a_						     =			ViTHybridImageProcessor.from_pretrained('google/vit-hybrid-base-bit-384'	)
         a_						     =			ViTHybridForImageClassification.from_pretrained('google/vit-hybrid-base-bit-384'   ,	device_map='auto'	)

         a_						     =			prepare_img()

         a_						     =			image_processor(images=UpperCamelCase__   ,	return_tensors='pt'	)
         a_						     =			model(**UpperCamelCase__	)
         a_						     =			outputs.logits
         # model predicts one of the 1000 ImageNet classes
         a_						     =			logits.argmax(-1	).item()

         self.assertTrue(model.config.idalabel[predicted_class_idx]   ,	'tabby, tabby cat'	)












'''simple docstring'''







import argparse

import numpy as np
import torch

from transformers import SpeechTaHifiGan, SpeechTaHifiGanConfig, logging


logging.set_verbosity_info()
__lowerCAmelCase        =							logging.get_logger("transformers.models.speecht5")


def       __UpperCamelCase					(      lowercase_       :  Optional[int]   ,       lowercase_       :  Dict   ,       lowercase_       :  Any   ):
       """simple docstring"""




       hf_model.apply_weight_norm()

       a_						     =			checkpoint['input_conv.weight_g']
       a_						     =			checkpoint['input_conv.weight_v']
       a_						     =			checkpoint['input_conv.bias']

       for i in range(len(config.upsample_rates   )   ):
              a_						     =			checkpoint[F'upsamples.{i}.1.weight_g']
              a_						     =			checkpoint[F'upsamples.{i}.1.weight_v']
              a_						     =			checkpoint[F'upsamples.{i}.1.bias']

       for i in range(len(config.upsample_rates   ) * len(config.resblock_kernel_sizes   )   ):
              for j in range(len(config.resblock_dilation_sizes   )   ):
                     a_						     =			checkpoint[F'blocks.{i}.convs1.{j}.1.weight_g']
                     a_						     =			checkpoint[F'blocks.{i}.convs1.{j}.1.weight_v']
                     a_						     =			checkpoint[F'blocks.{i}.convs1.{j}.1.bias']

                     a_						     =			checkpoint[F'blocks.{i}.convs2.{j}.1.weight_g']
                     a_						     =			checkpoint[F'blocks.{i}.convs2.{j}.1.weight_v']
                     a_						     =			checkpoint[F'blocks.{i}.convs2.{j}.1.bias']

       a_						     =			checkpoint['output_conv.1.weight_g']
       a_						     =			checkpoint['output_conv.1.weight_v']
       a_						     =			checkpoint['output_conv.1.bias']

       hf_model.remove_weight_norm()


@torch.no_grad()
def       __UpperCamelCase					(      lowercase_       :  List[Any]   ,       lowercase_       :  int   ,       lowercase_       :  Optional[Any]   ,       lowercase_       :  List[str]=None   ,       lowercase_       :  Tuple=None   ,       ):
       """simple docstring"""




       if config_path is not None:
              a_						     =			SpeechTaHifiGanConfig.from_pretrained(lowercase_   )
       else:
              a_						     =			SpeechTaHifiGanConfig()

       a_						     =			SpeechTaHifiGan(lowercase_   )

       a_						     =			torch.load(lowercase_   )
       load_weights(orig_checkpoint['model']['generator']   ,       lowercase_   ,       lowercase_   )

       a_						     =			np.load(lowercase_   )
       a_						     =			stats[0].reshape(-1   )
       a_						     =			stats[1].reshape(-1   )
       a_						     =			torch.from_numpy(lowercase_   ).float()
       a_						     =			torch.from_numpy(lowercase_   ).float()

       model.save_pretrained(lowercase_   )

       if repo_id:
              print('Pushing to the hub...'   )
              model.push_to_hub(lowercase_   )


if __name__ == "__main__":
  __lowerCAmelCase        =							argparse.ArgumentParser()
  parser.add_argument("--checkpoint_path", required=True, default=None, type=str, help="Path to original checkpoint")
  parser.add_argument("--stats_path", required=True, default=None, type=str, help="Path to stats.npy file")
  parser.add_argument("--config_path", default=None, type=str, help="Path to hf config.json of model to convert")
  parser.add_argument(
      "--pytorch_dump_folder_path", required=True, default=None, type=str, help="Path to the output PyTorch model."
  )
  parser.add_argument(
      "--push_to_hub", default=None, type=str, help="Where to upload the converted model on the 🤗 hub."
  )

  __lowerCAmelCase        =							parser.parse_args()
  convert_hifigan_checkpoint(
      args.checkpoint_path,
      args.stats_path,
      args.pytorch_dump_folder_path,
      args.config_path,
      args.push_to_hub,
  )




